Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer.

Journal: International journal of radiation oncology, biology, physics
PMID: 39147208

Abstract

PURPOSE: Conventional normal tissue complication probability (NTCP) models for patients with head and neck cancer are typically based on single-value variables, which, for radiation-induced xerostomia, are baseline xerostomia and mean salivary gland doses. This study aimed to improve the prediction of late xerostomia by using 3-dimensional information from radiation dose distributions, computed tomography imaging, organ-at-risk segmentations, and clinical variables with deep learning (DL).

Authors

  • Hung Chu
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Suzanne P M de Vette
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Hendrike Neh
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Nanna M Sijtsema
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Roel J H M Steenbakkers
  • Amy Moreno
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Johannes A Langendijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
  • Peter M A van Ooijen
    University Medical Center Groningen, Center for Medical Imaging - North East Netherlands, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
  • Clifton D Fuller
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Lisanne V van Dijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. Electronic address: l.v.van.dijk@umcg.nl.

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